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Smart Sensing and Advanced Machine Learning Based Emerging Intelligent Systems...
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Smart Sensing and Advanced Machine Learning Based Emerging Intelligent Systems (SMILES)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 11880

Special Issue Editors

Dr. Jinchang Ren

E-Mail Website
Guest Editor
National Subsea Centre, Robert Gordon University, Aberdeen, UK
Interests: hyperspectral imaging; remote sensing; computer vision; machine learning; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiyong Wang

E-Mail Website
Guest Editor
The University of Sydney
Interests: Multimedia computing; multimedia information retrieval; human computer interaction; remote sensing; pattern recognition
Prof. Zheng Wang

E-Mail Website
Guest Editor
Associate Professor, College of Intelligence and Computing, Tianjin University, Tianjin, China
Interests: hyperspectral imaging; video analytics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent systems, which are aware of the surrounding environment and respond to it, are at the forefront of technology. These systems integrate sensing, machine learning, and data analytics to enable machines to obtain data from the environment, analyse it, and then adapt their behaviour accordingly. They have already infiltrated everyday life in applications such as facial recognition systems, personalized online retail recommendations, and surgical robotics.

The highly adaptive nature of intelligent systems has led to their adoption in various research fields. In healthcare, they have been for prevention, diagnostics, and treatment, with applications including drug delivery, cancer treatment, biomechanics and bio signal analysis. They have also been used in assistive technologies for the elderly. Intelligent systems have also been applied in industry for automation, transportation, machine monitoring, visual inspection, and telecommunication. Furthermore, within the security domain they have been used for surveillance and identification. Intelligent systems are also being designed to be self-diagnostic, meaning they are capable of identifying issues within themselves.

Advances in intelligent systems depend on progress in both hardware and software technology, with developments in smart sensing and advanced machine learning both required. Time-effective processing is another area for improvement. However, advancements are challenging due to the inherent uncertainty and dynamics of the environments where systems operate. Furthermore, greater automation should be accompanied by increased ethical debate.

Topics of interest include but not limited to:

  • Emerging smart sensing technology for intelligent systems
  • Novel machine learning techniques for intelligent systems
  • IoT based intelligent systems
  • Self-diagnosing and self-perceptive intelligent systems
  • Smart sensing based human computer interaction
  • Computer vision and affective computing based intelligent systems
  • Assistive technologies for healthcare and industry
  • Novel applications and case studies for gaming, education, security, manufacturing, etc.

Prof. Jinchang Ren
Dr. Zhiyong Wang
Prof. Zheng Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

19 pages, 6055 KiB  
Article
Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification
by Leehter Yao and Tung-Bin Lin
Sensors 2021, 21(19), 6616; https://doi.org/10.3390/s21196616 - 04 Oct 2021
Cited by 7 | Viewed by 1994
Abstract
The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is proposed for multi-class imbalanced data classification. EMDO [...] Read more.
The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is proposed for multi-class imbalanced data classification. EMDO utilizes a set of ellipsoids to approximate the decision regions of the minority class. Furthermore, multi-objective particle swarm optimization (MOPSO) is integrated with the Gustafson–Kessel algorithm in EMDO to learn the size, center, and orientation of every ellipsoid. Synthetic minority samples are generated based on Mahalanobis distance within every ellipsoid. The number of synthetic minority samples generated by EMDO in every ellipsoid is determined based on the density of minority samples in every ellipsoid. The results of computer simulations conducted herein indicate that EMDO outperforms most of the widely used oversampling schemes. Full article
(This article belongs to the Special Issue Smart Sensing and Advanced Machine Learning Based Emerging Intelligent Systems (SMILES))
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20 pages, 6152 KiB  
Article
Multi-Level Context Pyramid Network for Visual Sentiment Analysis
by Haochun Ou, Chunmei Qing, Xiangmin Xu and Jianxiu Jin
Sensors 2021, 21(6), 2136; https://doi.org/10.3390/s21062136 - 18 Mar 2021
Cited by 13 | Viewed by 2900
Abstract
Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people’s emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of [...] Read more.
Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people’s emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%. Full article
(This article belongs to the Special Issue Smart Sensing and Advanced Machine Learning Based Emerging Intelligent Systems (SMILES))
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18 pages, 3725 KiB  
Article
Unsupervised Trademark Retrieval Method Based on Attention Mechanism
by Jiangzhong Cao, Yunfei Huang, Qingyun Dai and Wing-Kuen Ling
Sensors 2021, 21(5), 1894; https://doi.org/10.3390/s21051894 - 08 Mar 2021
Cited by 7 | Viewed by 2102
Abstract
Aiming at the high cost of data labeling and ignoring the internal relevance of features in existing trademark retrieval methods, this paper proposes an unsupervised trademark retrieval method based on attention mechanism. In the proposed method, the instance discrimination framework is adopted and [...] Read more.
Aiming at the high cost of data labeling and ignoring the internal relevance of features in existing trademark retrieval methods, this paper proposes an unsupervised trademark retrieval method based on attention mechanism. In the proposed method, the instance discrimination framework is adopted and a lightweight attention mechanism is introduced to allocate a more reasonable learning weight to key features. With an unsupervised way, this proposed method can obtain good feature representation of trademarks and improve the performance of trademark retrieval. Extensive comparative experiments on the METU trademark dataset are conducted. The experimental results show that the proposed method is significantly better than traditional trademark retrieval methods and most existing supervised learning methods. The proposed method obtained a smaller value of NAR (Normalized Average Rank) at 0.051, which verifies the effectiveness of the proposed method in trademark retrieval. Full article
(This article belongs to the Special Issue Smart Sensing and Advanced Machine Learning Based Emerging Intelligent Systems (SMILES))
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16 pages, 7498 KiB  
Article
A Multipath Fusion Strategy Based Single Shot Detector
by Shuyi Qu, Kaizhu Huang, Amir Hussain and Yannis Goulermas
Sensors 2021, 21(4), 1360; https://doi.org/10.3390/s21041360 - 15 Feb 2021
Viewed by 1763
Abstract
Object detection has wide applications in intelligent systems and sensor applications. Compared with two stage detectors, recent one stage counterparts are capable of running more efficiently with comparable accuracy, which satisfy the requirement of real-time processing. To further improve the accuracy of one [...] Read more.
Object detection has wide applications in intelligent systems and sensor applications. Compared with two stage detectors, recent one stage counterparts are capable of running more efficiently with comparable accuracy, which satisfy the requirement of real-time processing. To further improve the accuracy of one stage single shot detector (SSD), we propose a novel Multi-Path fusion Single Shot Detector (MPSSD). Different from other feature fusion methods, we exploit the connection among different scale representations in a pyramid manner. We propose feature fusion module to generate new feature pyramids based on multiscale features in SSD, and these pyramids are sent to our pyramid aggregation module for generating final features. These enhanced features have both localization and semantics information, thus improving the detection performance with little computation cost. A series of experiments on three benchmark datasets PASCAL VOC2007, VOC2012, and MS COCO demonstrate that our approach outperforms many state-of-the-art detectors both qualitatively and quantitatively. In particular, for input images with size 512 × 512, our method attains mean Average Precision (mAP) of 81.8% on VOC2007 test, 80.3% on VOC2012 test, and 33.1% mAP on COCO test-dev 2015. Full article
(This article belongs to the Special Issue Smart Sensing and Advanced Machine Learning Based Emerging Intelligent Systems (SMILES))
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16 pages, 2198 KiB  
Article
Compressing Deep Networks by Neuron Agglomerative Clustering
by Li-Na Wang, Wenxue Liu, Xiang Liu, Guoqiang Zhong, Partha Pratim Roy, Junyu Dong and Kaizhu Huang
Sensors 2020, 20(21), 6033; https://doi.org/10.3390/s20216033 - 23 Oct 2020
Cited by 4 | Viewed by 2253
Abstract
In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large storage space and long computational time, which make it difficult to [...] Read more.
In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large storage space and long computational time, which make it difficult to fully exploit many deep neural networks (DNNs), especially in scenarios in which computing resources are limited. In this paper, to tackle this problem, we introduce a method for compressing the structure and parameters of DNNs based on neuron agglomerative clustering (NAC). Specifically, we utilize the agglomerative clustering algorithm to find similar neurons, while these similar neurons and the connections linked to them are then agglomerated together. Using NAC, the number of parameters and the storage space of DNNs are greatly reduced, without the support of an extra library or hardware. Extensive experiments demonstrate that NAC is very effective for the neuron agglomeration of both the fully connected and convolutional layers, which are common building blocks of DNNs, delivering similar or even higher network accuracy. Specifically, on the benchmark CIFAR-10 and CIFAR-100 datasets, using NAC to compress the parameters of the original VGGNet by 92.96% and 81.10%, respectively, the compact network obtained still outperforms the original networks. Full article
(This article belongs to the Special Issue Smart Sensing and Advanced Machine Learning Based Emerging Intelligent Systems (SMILES))
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